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generate_op.py
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generate_op.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import math
import os
from pathlib import Path
import yaml
from filters import (
assert_dense_or_sr,
cartesian_prod_mapping,
delete_last_underline,
find_optinal_inputs_name,
get_infer_var_type_func,
to_composite_grad_opmaker_name,
to_input_name,
to_int_array_tensor_name,
to_int_array_tensors_name,
to_op_attr_type,
to_opmaker_name,
to_opmaker_name_cstr,
to_pascal_case,
to_scalar_tensor_name,
to_variable_names,
)
from jinja2 import Environment, FileSystemLoader, StrictUndefined
from parse_utils import to_named_dict
from tests_utils import (
is_base_op,
is_composite_op,
is_initializer_list,
is_only_composite_op,
is_scalar,
is_vec,
supports_inplace,
supports_no_need_buffer,
)
file_loader = FileSystemLoader(Path(__file__).parent / "templates")
env = Environment(
loader=file_loader,
keep_trailing_newline=True,
trim_blocks=True,
lstrip_blocks=True,
undefined=StrictUndefined,
extensions=['jinja2.ext.do'],
)
env.filters["to_op_attr_type"] = to_op_attr_type
env.filters["to_opmaker_name"] = to_opmaker_name
env.filters["to_pascal_case"] = to_pascal_case
env.filters["to_scalar_tensor_name"] = to_scalar_tensor_name
env.filters["to_int_array_tensor_name"] = to_int_array_tensor_name
env.filters["to_int_array_tensors_name"] = to_int_array_tensors_name
env.filters["to_input_name"] = to_input_name
env.filters["to_opmaker_name_cstr"] = to_opmaker_name_cstr
env.filters["cartesian_prod_mapping"] = cartesian_prod_mapping
env.filters["to_composite_grad_opmaker_name"] = to_composite_grad_opmaker_name
env.filters["to_variable_names"] = to_variable_names
env.filters["get_infer_var_type_func"] = get_infer_var_type_func
env.filters["assert_dense_or_sr"] = assert_dense_or_sr
env.filters["find_optinal_inputs_name"] = find_optinal_inputs_name
env.tests["base_op"] = is_base_op
env.tests["composite_op"] = is_composite_op
env.tests["only_composite_op"] = is_only_composite_op
env.tests["vec"] = is_vec
env.tests["scalar"] = is_scalar
env.tests["initializer_list"] = is_initializer_list
env.tests["supports_inplace"] = supports_inplace
env.tests["supports_no_need_buffer"] = supports_no_need_buffer
def restruct_io(op):
op["input_dict"] = to_named_dict(op["inputs"])
op["attr_dict"] = to_named_dict(op["attrs"])
op["output_dict"] = to_named_dict(op["outputs"])
return op
def process_scalar(op_item, scalar_configs):
scalar_map = {
'Scalar': 'float',
'Scalar(float)': 'float',
'Scalar(int)': 'int',
'Scalar(int64_t)': 'int64_t',
}
if scalar_configs is not None:
for attr_item in op_item['attrs']:
if attr_item['name'] in scalar_configs:
attr_type = attr_item['typename']
assert (
attr_type in scalar_map
), f"{op_item['name']}'s scalar in op_compat.yaml is error, the data_type of {attr_item['name']} is expected to be one of Scalar, Scalar(float), Scalar(int) or Scalar(int64_t), but now is {attr_type}."
scalar_config = scalar_configs[attr_item['name']]
attr_item['is_support_tensor'] = (
True
if 'support_tensor' in scalar_config
and scalar_config['support_tensor']
else False
)
attr_item['data_type'] = (
scalar_config['data_type']
if 'data_type' in scalar_config
else scalar_map[attr_type]
)
if attr_item['is_support_tensor'] is False:
attr_item['tensor_name'] = scalar_config['tensor_name']
def process_int_array(op_item, int_array_configs):
data_type_map = {
'int': 'std::vector<int>',
'int64_t': 'std::vector<int64_t>',
}
if int_array_configs is not None:
for attr_item in op_item['attrs']:
if attr_item['name'] in int_array_configs:
attr_type = attr_item['typename']
assert (
attr_item['typename'] == "IntArray"
), f"{op_item['name']}'s int_array in op_compat.yaml is error, the data_type of {attr_item['name']} is expected to be one of IntArray, but now is {attr_type}."
int_array_config = int_array_configs[attr_item['name']]
attr_item['is_support_tensor'] = (
True
if 'support_tensor' in int_array_config
and int_array_config['support_tensor']
else False
)
attr_item['data_type'] = (
data_type_map[int_array_config['data_type']]
if 'data_type' in int_array_config
else 'std::vector<int64_t>'
)
if attr_item['is_support_tensor'] is False:
attr_item['manual_flag'] = True
if 'tensor_name' in int_array_config:
attr_item['tensor_name'] = int_array_config[
'tensor_name'
]
if 'tensors_name' in int_array_config:
attr_item['tensors_name'] = int_array_config[
'tensors_name'
]
def add_composite_info(ops, backward_ops, backward_op_dict):
# add backward composite name in forward
for op in ops + backward_ops:
if (
op["backward"] in backward_op_dict
and "composite" in backward_op_dict[op["backward"]]
):
op["backward_composite"] = op["backward"]
else:
op["backward_composite"] = None
# add whether only composite
if (
op["backward_composite"] is not None
and "invoke" not in backward_op_dict[op["backward"]]
and "kernel" not in backward_op_dict[op["backward"]]
):
op["only_backward_composite"] = True
else:
op["only_backward_composite"] = False
# add fluid name in ops and backward ops info
def add_fluid_name(dict_list):
for item in dict_list:
item["fluid_name"] = item["name"]
# add fluid name of op and params for OpMaker
def add_compat_name(op_fluid_map_list, forward_op_dict, backward_op_dict):
def get_phi_and_fluid_op_name(op_item):
names = op_item.split('(')
if len(names) == 1:
return names[0].strip(), names[0].strip()
else:
return names[0].strip(), names[1].split(')')[0].strip()
def add_op_param_name(op_args, args_alias_map):
for item in op_args:
if item['name'] in args_alias_map:
item['fluid_name'] = args_alias_map[item['name']]
else:
item['fluid_name'] = item['name']
def add_grad_args_name(op_args, args_alias_map):
for item in op_args:
if (
item['name'].endswith('_grad')
and item['name'][:-5] in args_alias_map
):
args_alias_map[item['name']] = (
args_alias_map[item['name'][:-5]] + '_grad'
)
item['fluid_name'] = args_alias_map[item['name'][:-5]] + '_grad'
elif (
item['name'].endswith('_grad')
and item['name'][:-5] not in args_alias_map
):
item['fluid_name'] = item['name']
def get_param_list_alias(param_list, args_map):
return [
args_map[param] if param in args_map else param
for param in param_list
]
def update_common_params_name(
op_item, args_name_map, scalar_configs, int_array_configs
):
if 'inplace' in op_item and op_item['inplace']:
inplace_map = {}
for key, val in op_item['inplace'].items():
if key in args_map:
key = args_map[key]
if val in args_map:
val = args_map[val]
inplace_map[key] = val
op_item['inplace'] = inplace_map
if 'no_need_buffer' in op_item and op_item['no_need_buffer']:
op_item['no_need_buffer'] = get_param_list_alias(
op_item['no_need_buffer'], args_map
)
if 'data_transform' in op_item and op_item['data_transform']:
data_trans_item = op_item['data_transform']
if 'skip_transform' in data_trans_item:
data_trans_item['skip_transform'] = get_param_list_alias(
data_trans_item['skip_transform'], args_map
)
if 'support_trans_dtype' in data_trans_item:
data_trans_item['support_trans_dtype'] = get_param_list_alias(
data_trans_item['support_trans_dtype'], args_map
)
process_scalar(op_item, scalar_configs)
process_int_array(op_item, int_array_configs)
if 'invoke' in op_item:
op_item['invoke']['args'] = [
args_map[param.strip()]
if param.strip() in args_map
else param.strip()
for param in op_item['invoke']['args'].split(',')
]
return
elif 'composite' in op_item and 'kernel' not in op_item:
return
op_item['infer_meta']['param'] = get_param_list_alias(
op_item['infer_meta']['param'], args_name_map
)
op_item['kernel']['param'] = get_param_list_alias(
op_item['kernel']['param'], args_name_map
)
if op_item['kernel']['data_type']:
op_item['kernel']['data_type']['candidates'] = get_param_list_alias(
op_item['kernel']['data_type']['candidates'], args_name_map
)
if op_item['kernel']['backend']:
op_item['kernel']['backend']['candidates'] = get_param_list_alias(
op_item['kernel']['backend']['candidates'], args_name_map
)
if op_item['kernel']['layout']:
op_item['kernel']['layout']['candidates'] = get_param_list_alias(
op_item['kernel']['layout']['candidates'], args_name_map
)
def add_grad_op_compat_name(grad_op_item, args_name_map):
add_op_param_name(grad_op_item['inputs'], args_name_map)
add_op_param_name(grad_op_item['outputs'], args_name_map)
add_op_param_name(grad_op_item['attrs'], args_name_map)
add_op_param_name(grad_op_item['forward']['inputs'], args_name_map)
add_op_param_name(grad_op_item['forward']['outputs'], args_name_map)
add_op_param_name(grad_op_item['forward']['attrs'], args_name_map)
add_grad_args_name(grad_op_item['inputs'], args_map)
add_grad_args_name(grad_op_item['outputs'], args_map)
for op_args in op_fluid_map_list:
new_op_name, op_name = get_phi_and_fluid_op_name(op_args['op'])
if new_op_name not in forward_op_dict:
continue
forward_op_item = forward_op_dict[new_op_name]
has_backward = True if forward_op_item['backward'] else False
if has_backward:
backward_op_item = backward_op_dict[forward_op_item['backward']]
if new_op_name != op_name:
forward_op_item['op_name'] = op_name
# add complex promote infomation
if "complex_promote" in op_args:
forward_op_item["complex_promote"] = op_args["complex_promote"]
if has_backward:
backward_op_item["complex_promote"] = op_args["complex_promote"]
scalar_configs = None
int_array_configs = None
if 'scalar' in op_args:
scalar_configs = op_args['scalar']
if 'int_array' in op_args:
int_array_configs = op_args['int_array']
if 'extra' in op_args and 'outputs' in op_args['extra']:
for out_item in forward_op_item['outputs']:
if out_item['name'] in op_args['extra']['outputs']:
out_item['is_extra'] = True
if 'extra' in op_args and 'inputs' in op_args['extra']:
for input_item in forward_op_item['inputs']:
if input_item['name'] in op_args['extra']['inputs']:
input_item['is_extra'] = True
key_set = ['inputs', 'attrs', 'outputs']
args_map = {}
for key in key_set:
if key in op_args:
args_map.update(op_args[key])
for args_item in forward_op_item[key]:
if args_item['name'] in op_args[key]:
if (
scalar_configs
and args_item['name'] in scalar_configs
):
scalar_configs[
op_args[key][args_item['name']]
] = scalar_configs[args_item['name']]
if (
int_array_configs
and args_item['name'] in int_array_configs
):
int_array_configs[
op_args[key][args_item['name']]
] = int_array_configs[args_item['name']]
args_item['fluid_name'] = op_args[key][
args_item['name']
]
update_common_params_name(
forward_op_item, args_map, scalar_configs, int_array_configs
)
if has_backward:
# update fluid info in backward
add_grad_op_compat_name(backward_op_item, args_map)
update_common_params_name(
backward_op_item, args_map, scalar_configs, int_array_configs
)
if 'backward' not in op_args:
continue
backward_op_list = op_args['backward'].split(',')
phi_bw_op_name, bw_op_name = get_phi_and_fluid_op_name(
backward_op_list[0]
)
if (
forward_op_item["backward_composite"] is not None
and phi_bw_op_name != bw_op_name
):
forward_op_item["backward_composite"] = bw_op_name
forward_op_item['backward'] = bw_op_name
backward_op_item['op_name'] = bw_op_name
# for double grad
if len(backward_op_list) > 1:
(
phi_double_grad_op_name,
double_grad_op_name,
) = get_phi_and_fluid_op_name(backward_op_list[1])
double_grad_item = backward_op_dict[phi_double_grad_op_name]
if (
backward_op_item["backward_composite"] is not None
and phi_double_grad_op_name != double_grad_op_name
):
backward_op_item["backward_composite"] = double_grad_op_name
backward_op_item['backward'] = double_grad_op_name
double_grad_item['op_name'] = double_grad_op_name
add_grad_op_compat_name(double_grad_item, args_map)
update_common_params_name(
double_grad_item,
args_map,
scalar_configs,
int_array_configs,
)
# for triple grad
if len(backward_op_list) > 2:
(
phi_triple_grad_op_name,
triple_grad_op_name,
) = get_phi_and_fluid_op_name(backward_op_list[2])
triple_grad_item = backward_op_dict[phi_triple_grad_op_name]
if (
double_grad_item["backward_composite"] is not None
and phi_triple_grad_op_name != triple_grad_op_name
):
double_grad_item[
"backward_composite"
] = triple_grad_op_name
double_grad_item['backward'] = triple_grad_op_name
triple_grad_item['op_name'] = triple_grad_op_name
add_grad_op_compat_name(triple_grad_item, args_map)
update_common_params_name(
triple_grad_item,
args_map,
scalar_configs,
int_array_configs,
)
def process_invoke_op(forward_op_dict, backward_op_dict):
for bw_op in backward_op_dict.values():
if 'invoke' in bw_op:
invoke_op = bw_op['invoke']['func']
args_list = bw_op['invoke']['args']
args_index = 0
# backward invoke forward
if invoke_op in forward_op_dict:
reuse_op = forward_op_dict[invoke_op]
bw_op['invoke']['func'] = reuse_op['op_name']
bw_op['invoke']['inputs'] = []
bw_op['invoke']['attrs'] = []
bw_op['invoke']['outputs'] = []
for input_item in reuse_op['inputs']:
bw_op['invoke']['inputs'].append(
{
'fluid_name': input_item['fluid_name'],
'name': input_item['name'],
'value': args_list[args_index],
}
)
args_index = args_index + 1
bw_fluid_attrs_set = [
item['fluid_name'] for item in bw_op['attrs']
]
for attr in reuse_op['attrs']:
if args_index < len(args_list):
attr_value = (
f"this->GetAttr(\"{args_list[args_index]}\")"
if args_list[args_index] in bw_fluid_attrs_set
else args_list[args_index]
)
bw_op['invoke']['attrs'].append(
{
'name': attr['name'],
'fluid_name': attr['fluid_name'],
'value': attr_value,
}
)
args_index = args_index + 1
else:
break
for idx, output_item in enumerate(reuse_op['outputs']):
bw_op['invoke']['outputs'].append(
{
'name': output_item['name'],
'fluid_name': output_item['fluid_name'],
'value': bw_op['outputs'][idx]['fluid_name'],
}
)
def parse_drop_empty_grad(op_fluid_list: list, bw_op_dict: dict):
for op_comp_map in op_fluid_list:
if 'drop_empty_grad' in op_comp_map:
bw_names = [
bw_name.split('(')[0].strip()
for bw_name in op_comp_map['backward'].split(',')
]
for bw_name in bw_names:
# static_ops.yaml and ops.yaml use the common op_compat.yaml
if bw_name in bw_op_dict:
for out_grad in op_comp_map['drop_empty_grad']:
assert (
out_grad in bw_op_dict[bw_name]['output_dict']
), f'''
{bw_name} with {out_grad} is not existed in output_dict '''
bw_op_dict[bw_name]['output_dict'][out_grad][
'drop_empty_grad'
] = False
def parse_get_expected_kerneltype(
op_fluid_list: list, fw_op_dict: dict, bw_op_dict: dict
):
for op_comp_map in op_fluid_list:
if 'get_expected_kernel_type' in op_comp_map:
fw_name = op_comp_map['op'].split('(')[0].strip()
# deal the last underline of function name in op_comp_map['get_expected_kernel_type']
new_get_expected_kernel_type_func_map = {}
for (key, value) in op_comp_map['get_expected_kernel_type'].items():
new_get_expected_kernel_type_func_map[
delete_last_underline(key)
] = value
op_comp_map[
'get_expected_kernel_type'
] = new_get_expected_kernel_type_func_map
if fw_name in op_comp_map['get_expected_kernel_type']:
# static_ops.yaml and ops.yaml use the common op_compat.yaml
if fw_name in fw_op_dict:
fw_op_dict[fw_name][
"get_expected_kernel_type"
] = op_comp_map['get_expected_kernel_type'][fw_name]
if "backward" in op_comp_map:
bw_names = [
bw_name.split('(')[0].strip()
for bw_name in op_comp_map['backward'].split(',')
]
for bw_name in bw_names:
# static_ops.yaml and ops.yaml use the common op_compat.yaml
if (
bw_name in bw_op_dict
and bw_name in op_comp_map['get_expected_kernel_type']
):
bw_op_dict[bw_name][
"get_expected_kernel_type"
] = op_comp_map['get_expected_kernel_type'][bw_name]
def parse_keep_signature(
op_fluid_list: list, fw_op_dict: dict, bw_op_dict: dict
):
for op_comp_map in op_fluid_list:
if 'manual_signature' in op_comp_map:
for op_name in op_comp_map['manual_signature']:
op_name_without_last_underline = delete_last_underline(op_name)
if op_name_without_last_underline in fw_op_dict:
fw_op_dict[op_name_without_last_underline][
"manual_signature"
] = True
elif op_name_without_last_underline in bw_op_dict:
bw_op_dict[op_name_without_last_underline][
"manual_signature"
] = True
def split_ops_list(ops, backward_op_dict, split_num):
new_ops_list = []
new_bw_ops_list = []
list_size = math.ceil(len(ops) / split_num)
tmp_ops_list = []
tmp_bw_ops_list = []
for idx, op in enumerate(ops):
tmp_ops_list.append(op)
current_op = op
while (
'backward' in current_op
and current_op['backward'] in backward_op_dict
):
tmp_bw_ops_list.append(backward_op_dict[current_op['backward']])
current_op = backward_op_dict[current_op['backward']]
if (idx + 1) % list_size == 0 or idx == len(ops) - 1:
new_ops_list.append(tmp_ops_list)
new_bw_ops_list.append(tmp_bw_ops_list)
tmp_ops_list = []
tmp_bw_ops_list = []
return new_ops_list, new_bw_ops_list
def to_phi_and_fluid_op_name_without_underline(op_item):
'''
If the op_name ends with '_', delete the last '_'. For an example, 'sgd_' becomes 'sgd
'''
names = op_item.split('(')
if len(names) == 1:
op_kernel_name = delete_last_underline(names[0].strip())
return op_kernel_name
else:
op_name = delete_last_underline(names[0].strip())
kernel_name = delete_last_underline(names[1].split(')')[0].strip())
return op_name + '(' + kernel_name + ')'
def main(
ops_yaml_path,
backward_yaml_path,
op_compat_yaml_path,
op_version_yaml_path,
output_op_path,
output_arg_map_path,
):
with open(ops_yaml_path, "rt") as f:
ops = yaml.safe_load(f)
ops = [restruct_io(op) for op in ops]
forward_op_dict = to_named_dict(ops, True)
with open(backward_yaml_path, "rt") as f:
backward_ops = yaml.safe_load(f)
backward_ops = [restruct_io(op) for op in backward_ops]
backward_op_dict = to_named_dict(backward_ops, True)
with open(op_version_yaml_path, "rt") as f:
op_versions = yaml.safe_load(f)
# add op version info into op
for op_version in op_versions:
if op_version['op'] in forward_op_dict:
forward_op_dict[op_version['op']]['version'] = op_version['version']
with open(op_compat_yaml_path, "rt") as f:
op_fluid_map_list = yaml.safe_load(f)
for op_args in op_fluid_map_list:
op_args["op"] = to_phi_and_fluid_op_name_without_underline(
op_args["op"]
)
for op in ops:
op['op_name'] = op['name']
add_fluid_name(op['inputs'])
add_fluid_name(op['attrs'])
add_fluid_name(op['outputs'])
for bw_op in backward_ops:
bw_op['op_name'] = bw_op['name']
add_fluid_name(bw_op['inputs'])
add_fluid_name(bw_op['attrs'])
add_fluid_name(bw_op['outputs'])
add_fluid_name(bw_op['forward']['inputs'])
add_fluid_name(bw_op['forward']['attrs'])
add_fluid_name(bw_op['forward']['outputs'])
for bw_output in bw_op['outputs']:
bw_output['drop_empty_grad'] = True
# deal the drop_empty_grad of bw_op by op_compat.yaml
parse_drop_empty_grad(op_fluid_map_list, backward_op_dict)
parse_get_expected_kerneltype(
op_fluid_map_list, forward_op_dict, backward_op_dict
)
parse_keep_signature(op_fluid_map_list, forward_op_dict, backward_op_dict)
add_composite_info(ops, backward_ops, backward_op_dict)
add_compat_name(op_fluid_map_list, forward_op_dict, backward_op_dict)
# prepare for invoke case
process_invoke_op(forward_op_dict, backward_op_dict)
# fill backward field for an op if another op claims it as forward
for name, backward_op in backward_op_dict.items():
forward_name = backward_op["forward"]["name"]
if forward_name in backward_op_dict:
forward_op = backward_op_dict[forward_name]
if forward_op["backward"] is None:
forward_op["backward"] = name
op_dict = {}
op_dict.update(forward_op_dict)
op_dict.update(backward_op_dict)
if len(ops) == 0 and len(backward_ops) == 0:
if os.path.isfile(output_op_path):
os.remove(output_op_path)
if os.path.isfile(output_arg_map_path):
os.remove(output_arg_map_path)
return
op_template = env.get_template('op.c.j2')
backward_fluid_op_dict = {}
for bw_op in backward_ops:
backward_fluid_op_dict[bw_op['op_name']] = bw_op
output_op_files_num = len(output_op_path)
new_ops_list, new_bw_ops_list = split_ops_list(
ops, backward_fluid_op_dict, output_op_files_num
)
for idx, output_op_file in enumerate(output_op_path):
with open(output_op_file, "wt") as f:
msg = op_template.render(
ops=new_ops_list[idx],
backward_ops=new_bw_ops_list[idx],
op_dict=op_dict,
)
f.write(msg)
ks_template = env.get_template('ks.c.j2')
with open(output_arg_map_path, 'wt') as f:
msg = ks_template.render(ops=ops, backward_ops=backward_ops)
f.write(msg)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Generate operator file from op yaml."
)
parser.add_argument(
'--ops_yaml_path', type=str, help="parsed ops yaml file."
)
parser.add_argument(
'--backward_yaml_path', type=str, help="parsed backward ops yaml file."
)
parser.add_argument(
'--op_compat_yaml_path', type=str, help="ops args compat yaml file."
)
parser.add_argument(
'--op_version_yaml_path', type=str, help="ops version yaml file."
)
parser.add_argument(
"--output_op_path",
type=str,
nargs='+',
help="path to save generated operators.",
)
parser.add_argument(
"--output_arg_map_path",
type=str,
help="path to save generated argument mapping functions.",
)
args = parser.parse_args()
main(
args.ops_yaml_path,
args.backward_yaml_path,
args.op_compat_yaml_path,
args.op_version_yaml_path,
args.output_op_path,
args.output_arg_map_path,
)